Multi-Agent Simulation and Management Practices
Intelligent agents offer a new and exciting way of understanding the world of work. Agent-Based Simulation (ABS), one way of using intelligent agents, carries great potential for progressing our understanding of management practices and how they link to retail performance. We have developed simulati...
| Main Authors: | , , , |
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| Format: | Book Section |
| Published: |
IDEAS Group
2007
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| Online Access: | https://eprints.nottingham.ac.uk/644/ |
| _version_ | 1848790453302853632 |
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| author | Siebers, Peer-Olaf Aickelin, Uwe Celia, Helen Clegg, Christopher |
| author_facet | Siebers, Peer-Olaf Aickelin, Uwe Celia, Helen Clegg, Christopher |
| author_sort | Siebers, Peer-Olaf |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Intelligent agents offer a new and exciting way of understanding the world of work. Agent-Based Simulation (ABS), one way of using intelligent agents, carries great potential for progressing our understanding of management practices and how they link to retail performance. We have developed simulation models based on research by a multi-disciplinary team of economists, work psychologists and computer scientists. We will discuss our experiences of implementing these concepts working with a well-known retail department store.
There is no doubt that management practices are linked to the performance of an organisation (Reynolds et al., 2005; Wall & Wood, 2005). Best practices have been developed, but when it comes down to the actual application of these guidelines considerable ambiguity remains regarding their effectiveness within particular contexts (Siebers et al., forthcoming a).
Most Operational Research (OR) methods can only be used as analysis tools once management practices have been implemented. Often they are not very useful for giving answers to speculative ‘what-if’ questions, particularly when one is interested in the development of the system over time rather than just the state of the system at a certain point in time.
Simulation can be used to analyse the operation of dynamic and stochastic systems. ABS is particularly useful when complex interactions between system entities exist, such as autonomous decision making or negotiation. In an ABS model the researcher explicitly describes the decision process of simulated actors at the micro level. Structures emerge at the macro level as a result of the actions of the agents and their interactions with other agents and the environment.
We will show how ABS experiments can deal with testing and optimising management practices such as training, empowerment or teamwork. Hence, questions such as “will staff setting their own break times improve performance?” can be investigated. |
| first_indexed | 2025-11-14T18:12:51Z |
| format | Book Section |
| id | nottingham-644 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:12:51Z |
| publishDate | 2007 |
| publisher | IDEAS Group |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-6442020-05-04T20:29:10Z https://eprints.nottingham.ac.uk/644/ Multi-Agent Simulation and Management Practices Siebers, Peer-Olaf Aickelin, Uwe Celia, Helen Clegg, Christopher Intelligent agents offer a new and exciting way of understanding the world of work. Agent-Based Simulation (ABS), one way of using intelligent agents, carries great potential for progressing our understanding of management practices and how they link to retail performance. We have developed simulation models based on research by a multi-disciplinary team of economists, work psychologists and computer scientists. We will discuss our experiences of implementing these concepts working with a well-known retail department store. There is no doubt that management practices are linked to the performance of an organisation (Reynolds et al., 2005; Wall & Wood, 2005). Best practices have been developed, but when it comes down to the actual application of these guidelines considerable ambiguity remains regarding their effectiveness within particular contexts (Siebers et al., forthcoming a). Most Operational Research (OR) methods can only be used as analysis tools once management practices have been implemented. Often they are not very useful for giving answers to speculative ‘what-if’ questions, particularly when one is interested in the development of the system over time rather than just the state of the system at a certain point in time. Simulation can be used to analyse the operation of dynamic and stochastic systems. ABS is particularly useful when complex interactions between system entities exist, such as autonomous decision making or negotiation. In an ABS model the researcher explicitly describes the decision process of simulated actors at the micro level. Structures emerge at the macro level as a result of the actions of the agents and their interactions with other agents and the environment. We will show how ABS experiments can deal with testing and optimising management practices such as training, empowerment or teamwork. Hence, questions such as “will staff setting their own break times improve performance?” can be investigated. IDEAS Group 2007 Book Section PeerReviewed Siebers, Peer-Olaf, Aickelin, Uwe, Celia, Helen and Clegg, Christopher (2007) Multi-Agent Simulation and Management Practices. In: Encyclopedia of Decision Making and Decision Support Technologies. IDEAS Group. (In Press) |
| spellingShingle | Siebers, Peer-Olaf Aickelin, Uwe Celia, Helen Clegg, Christopher Multi-Agent Simulation and Management Practices |
| title | Multi-Agent Simulation and Management Practices |
| title_full | Multi-Agent Simulation and Management Practices |
| title_fullStr | Multi-Agent Simulation and Management Practices |
| title_full_unstemmed | Multi-Agent Simulation and Management Practices |
| title_short | Multi-Agent Simulation and Management Practices |
| title_sort | multi-agent simulation and management practices |
| url | https://eprints.nottingham.ac.uk/644/ |